CODEWITHSUNDEEP
javamachine-learningartificial-intelligenceneural-networkgenetic-algorithm
Zeeshan Chaudhry
Zeeshan Chaudhry

Reputation: 862

Neural network to solve a card-prob

The problem - I have 10 number of cards value 1 to 10. Now I have to arrange the cards in away that adding 5 cards gives me 36 and product of remaining 5 cards give me 360.

I had successfully made a GA to solve cards Problem in java. Now I am thinking to solve same problem with Neural Network. Is it possible to solve this by NN? What approach should I take?

Upvotes: 1

Views: 275

Answers (2)

Nathan S.
Nathan S.

Reputation: 5388

To follow up on @mikera's comments on why Neural Networks (NNs) might not be best for this task, it is useful to consider how NNs are usually used.

A NN is usually used in a supervised learning task. That is, the implementer provides many examples of input and the correct output that goes with that input. The NN then finds a general function which captures the provided input/output pairs and hopefully captures many other previously unseen input/output pairs as well.

In your problem you are solving a particular optimization, so there isn't much training to be done. There is just one (or more) right answers. So, NNs aren't really designed for such problems.

Note that the concept of not having a sum/product doesn't necessarily hurt a NN. You just have to create your own input layer which has sum and product features so that the NN can learn directly from these features. But, in this problem it won't help very much.

Note also that your problem is so small that even a naive enumeration of all combinations (10! = 3,628,800) of numbers should be achievable in a few seconds at most.

Upvotes: 1

mikera
mikera

Reputation: 106401

This problem is hard to solve directly with a Neural Network. Neural Networks are not going to have a concept of sum or product, so they won't be able to tell the difference between a valid and invalid solution directly.

If you created enough examples and labelled then then the neural network might be able to learn to tell the "good" and "bad" arrangements apart just by memorising them all. But it would be a very inefficient and inaccurate way of doing this, and it would be somewhat pointless - you'd have to have a separate program that knew how to solve the problem in order to create the data to train the neural network.

P.S. I think you are a bit lucky that you managed to get the GA to work as well - I suspect it only worked because the problem is small enough for the GA to try most of the possible solutions in the vicinity of the answer(s) and hence it stumbles upon a correct answer by chance before too long.

Upvotes: 4

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